Overview

Dataset statistics

Number of variables26
Number of observations66259
Missing cells349708
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.1 MiB
Average record size in memory208.0 B

Variable types

Categorical8
Text9
Numeric6
DateTime3

Alerts

Fuel Type Code has constant value ""Constant
Country has constant value ""Constant
EV Level1 EVSE Num is highly overall correlated with EV Level2 EVSE NumHigh correlation
EV Level2 EVSE Num is highly overall correlated with EV Level1 EVSE NumHigh correlation
Status Code is highly overall correlated with Groups With Access CodeHigh correlation
Groups With Access Code is highly overall correlated with Status Code and 1 other fieldsHigh correlation
EV Network is highly overall correlated with Access CodeHigh correlation
Access Code is highly overall correlated with Groups With Access Code and 1 other fieldsHigh correlation
Status Code is highly imbalanced (77.5%)Imbalance
Groups With Access Code is highly imbalanced (77.1%)Imbalance
EV Network is highly imbalanced (51.9%)Imbalance
Owner Type Code is highly imbalanced (62.9%)Imbalance
EV Connector Types is highly imbalanced (73.1%)Imbalance
Access Code is highly imbalanced (68.8%)Imbalance
Access Days Time has 7071 (10.7%) missing valuesMissing
Cards Accepted has 61229 (92.4%) missing valuesMissing
EV Level1 EVSE Num has 65529 (98.9%) missing valuesMissing
EV Level2 EVSE Num has 8164 (12.3%) missing valuesMissing
EV DC Fast Count has 57258 (86.4%) missing valuesMissing
Owner Type Code has 48259 (72.8%) missing valuesMissing
Facility Type has 49147 (74.2%) missing valuesMissing
EV Pricing has 52704 (79.5%) missing valuesMissing
EV Level2 EVSE Num is highly skewed (γ1 = 31.11234505)Skewed
ID has unique valuesUnique

Reproduction

Analysis started2023-10-12 09:04:50.086583
Analysis finished2023-10-12 09:04:59.508341
Duration9.42 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Fuel Type Code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
ELEC
66259 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters265036
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEC
2nd rowELEC
3rd rowELEC
4th rowELEC
5th rowELEC

Common Values

ValueCountFrequency (%)
ELEC 66259
100.0%

Length

2023-10-12T02:04:59.573016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T02:04:59.670500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
elec 66259
100.0%

Most occurring characters

ValueCountFrequency (%)
E 132518
50.0%
L 66259
25.0%
C 66259
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 265036
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 132518
50.0%
L 66259
25.0%
C 66259
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 265036
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 132518
50.0%
L 66259
25.0%
C 66259
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 265036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 132518
50.0%
L 66259
25.0%
C 66259
25.0%

Station Name
Text

Distinct63569
Distinct (%)95.9%
Missing1
Missing (%)< 0.1%
Memory size517.8 KiB
2023-10-12T02:05:00.039480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length116
Median length86
Mean length24.888014
Min length2

Characters and Unicode

Total characters1649030
Distinct characters101
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62065 ?
Unique (%)93.7%

Sample

1st rowLADWP - Truesdale Center
2nd rowLADWP - West LA District Office
3rd rowLos Angeles Convention Center
4th rowLADWP - John Ferraro Building
5th rowLADWP - Haynes Power Plant
ValueCountFrequency (%)
14958
 
5.3%
1 6611
 
2.3%
tesla 6202
 
2.2%
station 6110
 
2.2%
2 4712
 
1.7%
destination 4212
 
1.5%
of 2912
 
1.0%
city 2604
 
0.9%
center 2524
 
0.9%
garage 2133
 
0.8%
Other values (31833) 229813
81.3%
2023-10-12T02:05:00.517335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
217945
 
13.2%
e 74896
 
4.5%
a 64939
 
3.9%
A 61817
 
3.7%
T 60240
 
3.7%
E 56168
 
3.4%
S 53150
 
3.2%
n 52882
 
3.2%
t 49611
 
3.0%
r 49327
 
3.0%
Other values (91) 908055
55.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 716232
43.4%
Lowercase Letter 589497
35.7%
Space Separator 217947
 
13.2%
Decimal Number 87421
 
5.3%
Dash Punctuation 21609
 
1.3%
Other Punctuation 12874
 
0.8%
Open Punctuation 1418
 
0.1%
Close Punctuation 1411
 
0.1%
Connector Punctuation 469
 
< 0.1%
Math Symbol 77
 
< 0.1%
Other values (5) 75
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 61817
 
8.6%
T 60240
 
8.4%
E 56168
 
7.8%
S 53150
 
7.4%
C 48259
 
6.7%
O 47898
 
6.7%
R 45046
 
6.3%
N 42329
 
5.9%
I 38586
 
5.4%
L 36625
 
5.1%
Other values (21) 226114
31.6%
Lowercase Letter
ValueCountFrequency (%)
e 74896
12.7%
a 64939
11.0%
n 52882
9.0%
t 49611
8.4%
r 49327
8.4%
i 47006
8.0%
o 45869
 
7.8%
s 37647
 
6.4%
l 36650
 
6.2%
u 15491
 
2.6%
Other values (20) 115179
19.5%
Other Punctuation
ValueCountFrequency (%)
# 3528
27.4%
& 2432
18.9%
, 1972
15.3%
. 1723
13.4%
' 1324
 
10.3%
@ 1046
 
8.1%
/ 482
 
3.7%
: 247
 
1.9%
\ 51
 
0.4%
; 43
 
0.3%
Other values (3) 26
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 21897
25.0%
2 15933
18.2%
0 12247
14.0%
3 8610
 
9.8%
4 6974
 
8.0%
5 6828
 
7.8%
6 4651
 
5.3%
7 3840
 
4.4%
8 3315
 
3.8%
9 3126
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 21582
99.9%
– 24
 
0.1%
‑ 2
 
< 0.1%
— 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 68
88.3%
| 7
 
9.1%
= 2
 
2.6%
Space Separator
ValueCountFrequency (%)
217945
> 99.9%
  2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1418
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1411
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 469
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 62
100.0%
Control
ValueCountFrequency (%)
7
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 3
100.0%
Format
ValueCountFrequency (%)
​ 2
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1305728
79.2%
Common 343301
 
20.8%
Greek 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 74896
 
5.7%
a 64939
 
5.0%
A 61817
 
4.7%
T 60240
 
4.6%
E 56168
 
4.3%
S 53150
 
4.1%
n 52882
 
4.1%
t 49611
 
3.8%
r 49327
 
3.8%
C 48259
 
3.7%
Other values (50) 734439
56.2%
Common
ValueCountFrequency (%)
217945
63.5%
1 21897
 
6.4%
- 21582
 
6.3%
2 15933
 
4.6%
0 12247
 
3.6%
3 8610
 
2.5%
4 6974
 
2.0%
5 6828
 
2.0%
6 4651
 
1.4%
7 3840
 
1.1%
Other values (30) 22794
 
6.6%
Greek
ValueCountFrequency (%)
Γ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1648908
> 99.9%
Punctuation 91
 
< 0.1%
None 28
 
< 0.1%
Modifier Letters 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217945
 
13.2%
e 74896
 
4.5%
a 64939
 
3.9%
A 61817
 
3.7%
T 60240
 
3.7%
E 56168
 
3.4%
S 53150
 
3.2%
n 52882
 
3.2%
t 49611
 
3.0%
r 49327
 
3.0%
Other values (74) 907933
55.1%
Punctuation
ValueCountFrequency (%)
’ 62
68.1%
– 24
 
26.4%
​ 2
 
2.2%
‑ 2
 
2.2%
— 1
 
1.1%
None
ValueCountFrequency (%)
é 17
60.7%
  2
 
7.1%
ó 1
 
3.6%
Ñ 1
 
3.6%
® 1
 
3.6%
 1
 
3.6%
ū 1
 
3.6%
ñ 1
 
3.6%
Γ 1
 
3.6%
Ç 1
 
3.6%
Modifier Letters
ValueCountFrequency (%)
ʻ 3
100.0%

Street Address
Text

Distinct45419
Distinct (%)68.6%
Missing33
Missing (%)< 0.1%
Memory size517.8 KiB
2023-10-12T02:05:00.935294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length57
Mean length17.712092
Min length1

Characters and Unicode

Total characters1173001
Distinct characters95
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35663 ?
Unique (%)53.9%

Sample

1st row11797 Truesdale St
2nd row1394 S Sepulveda Blvd
3rd row1201 S Figueroa St
4th row111 N Hope St
5th row6801 E 2nd St
ValueCountFrequency (%)
st 14149
 
5.9%
ave 9330
 
3.9%
rd 8906
 
3.7%
dr 6171
 
2.6%
blvd 5377
 
2.2%
n 4264
 
1.8%
s 4230
 
1.8%
w 4161
 
1.7%
e 3911
 
1.6%
street 2496
 
1.0%
Other values (22072) 178446
73.9%
2023-10-12T02:05:01.381449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
175558
 
15.0%
e 71046
 
6.1%
a 55573
 
4.7%
t 54175
 
4.6%
r 51164
 
4.4%
0 49342
 
4.2%
1 47939
 
4.1%
n 41871
 
3.6%
o 38633
 
3.3%
l 35061
 
3.0%
Other values (85) 552639
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 562336
47.9%
Decimal Number 250037
21.3%
Uppercase Letter 176943
 
15.1%
Space Separator 175558
 
15.0%
Other Punctuation 5100
 
0.4%
Dash Punctuation 2724
 
0.2%
Control 122
 
< 0.1%
Open Punctuation 78
 
< 0.1%
Close Punctuation 71
 
< 0.1%
Math Symbol 23
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 71046
12.6%
a 55573
9.9%
t 54175
9.6%
r 51164
9.1%
n 41871
 
7.4%
o 38633
 
6.9%
l 35061
 
6.2%
i 34598
 
6.2%
d 29970
 
5.3%
v 22441
 
4.0%
Other values (25) 127804
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 30747
17.4%
A 15137
 
8.6%
R 14624
 
8.3%
W 12391
 
7.0%
C 12226
 
6.9%
B 11542
 
6.5%
D 10353
 
5.9%
N 9278
 
5.2%
P 9063
 
5.1%
E 8902
 
5.0%
Other values (16) 42680
24.1%
Other Punctuation
ValueCountFrequency (%)
. 3009
59.0%
@ 981
 
19.2%
, 390
 
7.6%
# 234
 
4.6%
& 191
 
3.7%
' 170
 
3.3%
/ 86
 
1.7%
; 33
 
0.6%
" 4
 
0.1%
? 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 49342
19.7%
1 47939
19.2%
2 28458
11.4%
5 27895
11.2%
3 21830
8.7%
4 18261
 
7.3%
7 14684
 
5.9%
6 14609
 
5.8%
9 13653
 
5.5%
8 13366
 
5.3%
Control
ValueCountFrequency (%)
107
87.7%
10
 
8.2%
5
 
4.1%
Math Symbol
ValueCountFrequency (%)
+ 22
95.7%
| 1
 
4.3%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
¼ 1
33.3%
Space Separator
ValueCountFrequency (%)
175558
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2724
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 4
100.0%
Format
ValueCountFrequency (%)
​ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 739279
63.0%
Common 433722
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 71046
 
9.6%
a 55573
 
7.5%
t 54175
 
7.3%
r 51164
 
6.9%
n 41871
 
5.7%
o 38633
 
5.2%
l 35061
 
4.7%
i 34598
 
4.7%
S 30747
 
4.2%
d 29970
 
4.1%
Other values (51) 296441
40.1%
Common
ValueCountFrequency (%)
175558
40.5%
0 49342
 
11.4%
1 47939
 
11.1%
2 28458
 
6.6%
5 27895
 
6.4%
3 21830
 
5.0%
4 18261
 
4.2%
7 14684
 
3.4%
6 14609
 
3.4%
9 13653
 
3.1%
Other values (24) 21493
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1172953
> 99.9%
None 42
 
< 0.1%
Punctuation 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175558
 
15.0%
e 71046
 
6.1%
a 55573
 
4.7%
t 54175
 
4.6%
r 51164
 
4.4%
0 49342
 
4.2%
1 47939
 
4.1%
n 41871
 
3.6%
o 38633
 
3.3%
l 35061
 
3.0%
Other values (72) 552591
47.1%
None
ValueCountFrequency (%)
í 23
54.8%
ñ 8
 
19.0%
é 2
 
4.8%
½ 2
 
4.8%
ú 1
 
2.4%
á 1
 
2.4%
ā 1
 
2.4%
ō 1
 
2.4%
ū 1
 
2.4%
ó 1
 
2.4%
Punctuation
ValueCountFrequency (%)
’ 4
66.7%
​ 2
33.3%

City
Text

Distinct6451
Distinct (%)9.7%
Missing4
Missing (%)< 0.1%
Memory size517.8 KiB
2023-10-12T02:05:01.624411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length29
Mean length8.989797
Min length2

Characters and Unicode

Total characters595619
Distinct characters69
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2274 ?
Unique (%)3.4%

Sample

1st rowSun Valley
2nd rowLos Angeles
3rd rowLos Angeles
4th rowLos Angeles
5th rowLong Beach
ValueCountFrequency (%)
san 2828
 
3.2%
city 2101
 
2.4%
los 1873
 
2.1%
angeles 1808
 
2.0%
beach 1265
 
1.4%
park 1184
 
1.3%
santa 1012
 
1.1%
new 841
 
0.9%
diego 761
 
0.9%
fort 679
 
0.8%
Other values (5206) 74559
83.9%
2023-10-12T02:05:01.954509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55645
 
9.3%
e 54472
 
9.1%
n 45517
 
7.6%
o 44838
 
7.5%
l 37919
 
6.4%
r 35063
 
5.9%
i 32981
 
5.5%
t 32215
 
5.4%
s 28104
 
4.7%
22659
 
3.8%
Other values (59) 206206
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 481812
80.9%
Uppercase Letter 90596
 
15.2%
Space Separator 22659
 
3.8%
Other Punctuation 384
 
0.1%
Dash Punctuation 152
 
< 0.1%
Decimal Number 14
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55645
11.5%
e 54472
11.3%
n 45517
9.4%
o 44838
9.3%
l 37919
 
7.9%
r 35063
 
7.3%
i 32981
 
6.8%
t 32215
 
6.7%
s 28104
 
5.8%
d 13616
 
2.8%
Other values (19) 101442
21.1%
Uppercase Letter
ValueCountFrequency (%)
S 10614
 
11.7%
C 9146
 
10.1%
B 7179
 
7.9%
A 7026
 
7.8%
L 6730
 
7.4%
M 5712
 
6.3%
P 5665
 
6.3%
H 3932
 
4.3%
W 3844
 
4.2%
R 3735
 
4.1%
Other values (16) 27013
29.8%
Decimal Number
ValueCountFrequency (%)
4 2
14.3%
8 2
14.3%
6 2
14.3%
1 2
14.3%
0 2
14.3%
2 2
14.3%
9 1
7.1%
5 1
7.1%
Other Punctuation
ValueCountFrequency (%)
. 342
89.1%
' 26
 
6.8%
, 16
 
4.2%
Space Separator
ValueCountFrequency (%)
22659
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 572408
96.1%
Common 23211
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55645
 
9.7%
e 54472
 
9.5%
n 45517
 
8.0%
o 44838
 
7.8%
l 37919
 
6.6%
r 35063
 
6.1%
i 32981
 
5.8%
t 32215
 
5.6%
s 28104
 
4.9%
d 13616
 
2.4%
Other values (45) 192038
33.5%
Common
ValueCountFrequency (%)
22659
97.6%
. 342
 
1.5%
- 152
 
0.7%
' 26
 
0.1%
, 16
 
0.1%
’ 2
 
< 0.1%
4 2
 
< 0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
1 2
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 595608
> 99.9%
None 9
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55645
 
9.3%
e 54472
 
9.1%
n 45517
 
7.6%
o 44838
 
7.5%
l 37919
 
6.4%
r 35063
 
5.9%
i 32981
 
5.5%
t 32215
 
5.4%
s 28104
 
4.7%
22659
 
3.8%
Other values (55) 206195
34.6%
None
ValueCountFrequency (%)
ñ 6
66.7%
ā 2
 
22.2%
ó 1
 
11.1%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

State
Text

Distinct54
Distinct (%)0.1%
Missing10
Missing (%)< 0.1%
Memory size517.8 KiB
2023-10-12T02:05:02.097757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters132498
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 17245
26.0%
ny 4092
 
6.2%
fl 3587
 
5.4%
tx 3322
 
5.0%
ma 2997
 
4.5%
wa 2298
 
3.5%
co 2257
 
3.4%
ga 1966
 
3.0%
md 1806
 
2.7%
pa 1761
 
2.7%
Other values (44) 24918
37.6%
2023-10-12T02:05:02.301866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 30485
23.0%
C 22804
17.2%
N 10641
 
8.0%
M 9331
 
7.0%
O 6720
 
5.1%
T 6493
 
4.9%
L 5634
 
4.3%
I 5265
 
4.0%
Y 4511
 
3.4%
F 3587
 
2.7%
Other values (15) 27027
20.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 132498
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 30485
23.0%
C 22804
17.2%
N 10641
 
8.0%
M 9331
 
7.0%
O 6720
 
5.1%
T 6493
 
4.9%
L 5634
 
4.3%
I 5265
 
4.0%
Y 4511
 
3.4%
F 3587
 
2.7%
Other values (15) 27027
20.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 132498
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 30485
23.0%
C 22804
17.2%
N 10641
 
8.0%
M 9331
 
7.0%
O 6720
 
5.1%
T 6493
 
4.9%
L 5634
 
4.3%
I 5265
 
4.0%
Y 4511
 
3.4%
F 3587
 
2.7%
Other values (15) 27027
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 30485
23.0%
C 22804
17.2%
N 10641
 
8.0%
M 9331
 
7.0%
O 6720
 
5.1%
T 6493
 
4.9%
L 5634
 
4.3%
I 5265
 
4.0%
Y 4511
 
3.4%
F 3587
 
2.7%
Other values (15) 27027
20.4%

ZIP
Text

Distinct11133
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:02.792004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9976607
Min length1

Characters and Unicode

Total characters331140
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3513 ?
Unique (%)5.3%

Sample

1st row91352
2nd row90024
3rd row90015
4th row90012
5th row90803
ValueCountFrequency (%)
94025 399
 
0.6%
92618 298
 
0.4%
95054 287
 
0.4%
92101 169
 
0.3%
98004 163
 
0.2%
92802 155
 
0.2%
94128 136
 
0.2%
95814 135
 
0.2%
94607 126
 
0.2%
98109 122
 
0.2%
Other values (11124) 64272
97.0%
2023-10-12T02:05:03.219805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54811
16.6%
1 41419
12.5%
2 39675
12.0%
9 33631
10.2%
3 32226
9.7%
4 30741
9.3%
5 26611
8.0%
8 24793
7.5%
7 23721
7.2%
6 23496
7.1%
Other values (10) 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331124
> 99.9%
Uppercase Letter 13
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54811
16.6%
1 41419
12.5%
2 39675
12.0%
9 33631
10.2%
3 32226
9.7%
4 30741
9.3%
5 26611
8.0%
8 24793
7.5%
7 23721
7.2%
6 23496
7.1%
Uppercase Letter
ValueCountFrequency (%)
N 3
23.1%
A 2
15.4%
G 2
15.4%
M 1
 
7.7%
I 1
 
7.7%
E 1
 
7.7%
C 1
 
7.7%
V 1
 
7.7%
P 1
 
7.7%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 331127
> 99.9%
Latin 13
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54811
16.6%
1 41419
12.5%
2 39675
12.0%
9 33631
10.2%
3 32226
9.7%
4 30741
9.3%
5 26611
8.0%
8 24793
7.5%
7 23721
7.2%
6 23496
7.1%
Latin
ValueCountFrequency (%)
N 3
23.1%
A 2
15.4%
G 2
15.4%
M 1
 
7.7%
I 1
 
7.7%
E 1
 
7.7%
C 1
 
7.7%
V 1
 
7.7%
P 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54811
16.6%
1 41419
12.5%
2 39675
12.0%
9 33631
10.2%
3 32226
9.7%
4 30741
9.3%
5 26611
8.0%
8 24793
7.5%
7 23721
7.2%
6 23496
7.1%
Other values (10) 16
 
< 0.1%

Status Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
E
62041 
T
 
4068
P
 
150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 62041
93.6%
T 4068
 
6.1%
P 150
 
0.2%

Length

2023-10-12T02:05:03.325363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T02:05:03.397998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
e 62041
93.6%
t 4068
 
6.1%
p 150
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 62041
93.6%
T 4068
 
6.1%
P 150
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66259
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 62041
93.6%
T 4068
 
6.1%
P 150
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 66259
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 62041
93.6%
T 4068
 
6.1%
P 150
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 62041
93.6%
T 4068
 
6.1%
P 150
 
0.2%

Groups With Access Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
Public
54391 
TEMPORARILY UNAVAILABLE (Public)
 
4027
Private
 
2943
Public - Credit card at all times
 
2556
Public - Call ahead
 
1343
Other values (22)
 
999

Length

Max length65
Median length6
Mean length9.2678881
Min length6

Characters and Unicode

Total characters614081
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowPublic
4th rowPrivate
5th rowPrivate

Common Values

ValueCountFrequency (%)
Public 54391
82.1%
TEMPORARILY UNAVAILABLE (Public) 4027
 
6.1%
Private 2943
 
4.4%
Public - Credit card at all times 2556
 
3.9%
Public - Call ahead 1343
 
2.0%
Private - Government only 712
 
1.1%
PLANNED - not yet accessible (Public) 129
 
0.2%
Public - Card key at all times 36
 
0.1%
Private - Credit card at all times 33
 
< 0.1%
TEMPORARILY UNAVAILABLE (Private) 15
 
< 0.1%
Other values (17) 74
 
0.1%

Length

2023-10-12T02:05:03.468760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
public 62529
64.2%
4895
 
5.0%
unavailable 4068
 
4.2%
temporarily 4068
 
4.2%
private 3729
 
3.8%
card 2650
 
2.7%
at 2647
 
2.7%
all 2647
 
2.7%
times 2647
 
2.7%
credit 2611
 
2.7%
Other values (15) 4848
 
5.0%

Most occurring characters

ValueCountFrequency (%)
i 71680
11.7%
l 71437
11.6%
P 70476
11.5%
c 65454
10.7%
b 62679
10.2%
u 62552
10.2%
31080
 
5.1%
A 16422
 
2.7%
a 15910
 
2.6%
t 12685
 
2.1%
Other values (31) 133706
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 408123
66.5%
Uppercase Letter 161549
 
26.3%
Space Separator 31080
 
5.1%
Dash Punctuation 4895
 
0.8%
Close Punctuation 4217
 
0.7%
Open Punctuation 4217
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 71680
17.6%
l 71437
17.5%
c 65454
16.0%
b 62679
15.4%
u 62552
15.3%
a 15910
 
3.9%
t 12685
 
3.1%
e 12316
 
3.0%
r 9729
 
2.4%
d 6629
 
1.6%
Other values (9) 17052
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
P 70476
43.6%
A 16422
 
10.2%
L 12360
 
7.7%
E 8286
 
5.1%
R 8137
 
5.0%
I 8136
 
5.0%
N 4368
 
2.7%
B 4068
 
2.5%
V 4068
 
2.5%
U 4068
 
2.5%
Other values (8) 21160
 
13.1%
Space Separator
ValueCountFrequency (%)
31080
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4895
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4217
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 569672
92.8%
Common 44409
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 71680
12.6%
l 71437
12.5%
P 70476
12.4%
c 65454
11.5%
b 62679
11.0%
u 62552
11.0%
A 16422
 
2.9%
a 15910
 
2.8%
t 12685
 
2.2%
L 12360
 
2.2%
Other values (27) 108017
19.0%
Common
ValueCountFrequency (%)
31080
70.0%
- 4895
 
11.0%
) 4217
 
9.5%
( 4217
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 71680
11.7%
l 71437
11.6%
P 70476
11.5%
c 65454
10.7%
b 62679
10.2%
u 62552
10.2%
31080
 
5.1%
A 16422
 
2.7%
a 15910
 
2.6%
t 12685
 
2.1%
Other values (31) 133706
21.8%

Access Days Time
Text

MISSING 

Distinct1415
Distinct (%)2.4%
Missing7071
Missing (%)10.7%
Memory size517.8 KiB
2023-10-12T02:05:03.808716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length159
Median length14
Mean length22.759529
Min length4

Characters and Unicode

Total characters1347091
Distinct characters75
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique998 ?
Unique (%)1.7%

Sample

1st rowFleet use only
2nd row5:30am-9pm; pay lot
3rd rowFor fleet and employee use only
4th rowFleet use only
5th rowFleet use only
ValueCountFrequency (%)
hours 54411
20.5%
daily 52502
19.8%
24 52154
19.7%
19443
 
7.3%
5:00am 9086
 
3.4%
10:59pm 8743
 
3.3%
12:00am 5925
 
2.2%
7:00am 2890
 
1.1%
fri 2795
 
1.1%
thu 2794
 
1.1%
Other values (917) 54578
20.6%
2023-10-12T02:05:04.278031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206134
 
15.3%
a 87254
 
6.5%
u 68751
 
5.1%
s 68228
 
5.1%
o 67606
 
5.0%
r 67596
 
5.0%
0 67289
 
5.0%
i 66115
 
4.9%
h 60707
 
4.5%
l 60312
 
4.5%
Other values (65) 527099
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 790421
58.7%
Decimal Number 244025
 
18.1%
Space Separator 206134
 
15.3%
Other Punctuation 58765
 
4.4%
Uppercase Letter 27147
 
2.0%
Dash Punctuation 20572
 
1.5%
Open Punctuation 11
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Currency Symbol 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 87254
11.0%
u 68751
8.7%
s 68228
8.6%
o 67606
8.6%
r 67596
8.6%
i 66115
8.4%
h 60707
7.7%
l 60312
7.6%
d 56950
7.2%
y 56563
7.2%
Other values (15) 130339
16.5%
Uppercase Letter
ValueCountFrequency (%)
S 5926
21.8%
T 5791
21.3%
F 3687
13.6%
M 3054
11.2%
W 2828
10.4%
D 1955
 
7.2%
C 646
 
2.4%
L 607
 
2.2%
J 579
 
2.1%
E 517
 
1.9%
Other values (15) 1557
 
5.7%
Decimal Number
ValueCountFrequency (%)
0 67289
27.6%
2 59224
24.3%
4 53290
21.8%
1 20737
 
8.5%
5 20301
 
8.3%
9 10949
 
4.5%
7 4913
 
2.0%
6 3572
 
1.5%
8 2107
 
0.9%
3 1643
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 38968
66.3%
; 19331
32.9%
. 252
 
0.4%
, 182
 
0.3%
/ 15
 
< 0.1%
& 10
 
< 0.1%
! 4
 
< 0.1%
' 2
 
< 0.1%
@ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
206134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20572
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 817568
60.7%
Common 529523
39.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 87254
10.7%
u 68751
8.4%
s 68228
8.3%
o 67606
8.3%
r 67596
8.3%
i 66115
8.1%
h 60707
 
7.4%
l 60312
 
7.4%
d 56950
 
7.0%
y 56563
 
6.9%
Other values (40) 157486
19.3%
Common
ValueCountFrequency (%)
206134
38.9%
0 67289
 
12.7%
2 59224
 
11.2%
4 53290
 
10.1%
: 38968
 
7.4%
1 20737
 
3.9%
- 20572
 
3.9%
5 20301
 
3.8%
; 19331
 
3.7%
9 10949
 
2.1%
Other values (15) 12728
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206134
 
15.3%
a 87254
 
6.5%
u 68751
 
5.1%
s 68228
 
5.1%
o 67606
 
5.0%
r 67596
 
5.0%
0 67289
 
5.0%
i 66115
 
4.9%
h 60707
 
4.5%
l 60312
 
4.5%
Other values (65) 527099
39.1%

Cards Accepted
Text

MISSING 

Distinct95
Distinct (%)1.9%
Missing61229
Missing (%)92.4%
Memory size517.8 KiB
2023-10-12T02:05:04.411755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length67
Median length13
Mean length15.619682
Min length1

Characters and Unicode

Total characters78567
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.9%

Sample

1st rowD
2nd rowA Cash D M V
3rd rowCREDIT
4th rowA D M V
5th rowA D M V
ValueCountFrequency (%)
m 4670
19.6%
d 4532
19.0%
a 4517
18.9%
v 4360
18.3%
debit 2939
12.3%
credit 1103
 
4.6%
account_balance 714
 
3.0%
apple_pay 413
 
1.7%
android_pay 380
 
1.6%
proprietor 86
 
0.4%
Other values (9) 156
 
0.7%
2023-10-12T02:05:04.633947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18840
24.0%
D 9334
11.9%
A 8245
10.5%
M 4673
 
5.9%
V 4364
 
5.6%
C 3373
 
4.3%
e 3090
 
3.9%
i 3046
 
3.9%
t 3045
 
3.9%
b 2939
 
3.7%
Other values (29) 17618
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 45026
57.3%
Space Separator 18840
24.0%
Lowercase Letter 13175
 
16.8%
Connector Punctuation 1526
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3090
23.5%
i 3046
23.1%
t 3045
23.1%
b 2939
22.3%
r 283
 
2.1%
o 176
 
1.3%
h 134
 
1.0%
s 115
 
0.9%
p 105
 
0.8%
a 70
 
0.5%
Other values (9) 172
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
D 9334
20.7%
A 8245
18.3%
M 4673
10.4%
V 4364
9.7%
C 3373
 
7.5%
E 2250
 
5.0%
N 1820
 
4.0%
T 1818
 
4.0%
P 1705
 
3.8%
R 1483
 
3.3%
Other values (8) 5961
13.2%
Space Separator
ValueCountFrequency (%)
18840
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58201
74.1%
Common 20366
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 9334
16.0%
A 8245
14.2%
M 4673
 
8.0%
V 4364
 
7.5%
C 3373
 
5.8%
e 3090
 
5.3%
i 3046
 
5.2%
t 3045
 
5.2%
b 2939
 
5.0%
E 2250
 
3.9%
Other values (27) 13842
23.8%
Common
ValueCountFrequency (%)
18840
92.5%
_ 1526
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18840
24.0%
D 9334
11.9%
A 8245
10.5%
M 4673
 
5.9%
V 4364
 
5.6%
C 3373
 
4.3%
e 3090
 
3.9%
i 3046
 
3.9%
t 3045
 
3.9%
b 2939
 
3.7%
Other values (29) 17618
22.4%

EV Level1 EVSE Num
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)5.5%
Missing65529
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean4.4273973
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:04.730232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile18
Maximum121
Range120
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.5101312
Coefficient of variation (CV)2.1480185
Kurtosis49.318925
Mean4.4273973
Median Absolute Deviation (MAD)1
Skewness5.9907166
Sum3232
Variance90.442595
MonotonicityNot monotonic
2023-10-12T02:05:04.812522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 342
 
0.5%
2 176
 
0.3%
4 50
 
0.1%
3 33
 
< 0.1%
10 16
 
< 0.1%
6 15
 
< 0.1%
5 15
 
< 0.1%
12 11
 
< 0.1%
8 10
 
< 0.1%
7 7
 
< 0.1%
Other values (30) 55
 
0.1%
(Missing) 65529
98.9%
ValueCountFrequency (%)
1 342
0.5%
2 176
0.3%
3 33
 
< 0.1%
4 50
 
0.1%
5 15
 
< 0.1%
6 15
 
< 0.1%
7 7
 
< 0.1%
8 10
 
< 0.1%
9 2
 
< 0.1%
10 16
 
< 0.1%
ValueCountFrequency (%)
121 1
< 0.1%
90 1
< 0.1%
71 1
< 0.1%
60 1
< 0.1%
54 2
< 0.1%
51 1
< 0.1%
49 2
< 0.1%
47 1
< 0.1%
42 1
< 0.1%
41 1
< 0.1%

EV Level2 EVSE Num
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct71
Distinct (%)0.1%
Missing8164
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean2.3655392
Minimum1
Maximum338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:04.898878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum338
Range337
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1423339
Coefficient of variation (CV)1.3283796
Kurtosis2459.4513
Mean2.3655392
Median Absolute Deviation (MAD)0
Skewness31.112345
Sum137426
Variance9.8742626
MonotonicityNot monotonic
2023-10-12T02:05:04.983297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37079
56.0%
1 12562
 
19.0%
4 3360
 
5.1%
3 1956
 
3.0%
6 977
 
1.5%
8 484
 
0.7%
5 434
 
0.7%
10 318
 
0.5%
12 145
 
0.2%
7 137
 
0.2%
Other values (61) 643
 
1.0%
(Missing) 8164
 
12.3%
ValueCountFrequency (%)
1 12562
 
19.0%
2 37079
56.0%
3 1956
 
3.0%
4 3360
 
5.1%
5 434
 
0.7%
6 977
 
1.5%
7 137
 
0.2%
8 484
 
0.7%
9 105
 
0.2%
10 318
 
0.5%
ValueCountFrequency (%)
338 1
< 0.1%
123 1
< 0.1%
108 1
< 0.1%
100 2
< 0.1%
98 1
< 0.1%
95 1
< 0.1%
91 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
73 1
< 0.1%

EV DC Fast Count
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)0.5%
Missing57258
Missing (%)86.4%
Infinite0
Infinite (%)0.0%
Mean4.0027775
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:05.071962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile12
Maximum84
Range83
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.9411529
Coefficient of variation (CV)1.2344311
Kurtosis27.130241
Mean4.0027775
Median Absolute Deviation (MAD)1
Skewness3.5964317
Sum36029
Variance24.414992
MonotonicityNot monotonic
2023-10-12T02:05:05.151007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 4350
 
6.6%
4 1121
 
1.7%
8 981
 
1.5%
2 979
 
1.5%
12 444
 
0.7%
6 326
 
0.5%
3 223
 
0.3%
16 168
 
0.3%
10 142
 
0.2%
20 69
 
0.1%
Other values (33) 198
 
0.3%
(Missing) 57258
86.4%
ValueCountFrequency (%)
1 4350
6.6%
2 979
 
1.5%
3 223
 
0.3%
4 1121
 
1.7%
5 22
 
< 0.1%
6 326
 
0.5%
7 19
 
< 0.1%
8 981
 
1.5%
9 3
 
< 0.1%
10 142
 
0.2%
ValueCountFrequency (%)
84 1
< 0.1%
76 1
< 0.1%
62 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
51 2
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
46 1
< 0.1%
44 1
< 0.1%

EV Network
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct40
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size517.8 KiB
ChargePoint Network
35000 
Non-Networked
9425 
Blink Network
6183 
Tesla Destination
4202 
Tesla
 
1967
Other values (35)
9479 

Length

Max length19
Median length19
Mean length15.71583
Min length3

Characters and Unicode

Total characters1041268
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHELL_RECHARGE
2nd rowNon-Networked
3rd rowNon-Networked
4th rowNon-Networked
5th rowNon-Networked

Common Values

ValueCountFrequency (%)
ChargePoint Network 35000
52.8%
Non-Networked 9425
 
14.2%
Blink Network 6183
 
9.3%
Tesla Destination 4202
 
6.3%
Tesla 1967
 
3.0%
Volta 1453
 
2.2%
EV Connect 1280
 
1.9%
SHELL_RECHARGE 1183
 
1.8%
eVgo Network 960
 
1.4%
Electrify America 921
 
1.4%
Other values (30) 3682
 
5.6%

Length

2023-10-12T02:05:05.234681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
network 42143
36.7%
chargepoint 35000
30.5%
non-networked 9425
 
8.2%
blink 6183
 
5.4%
tesla 6169
 
5.4%
destination 4202
 
3.7%
volta 1453
 
1.3%
ev 1280
 
1.1%
connect 1280
 
1.1%
shell_recharge 1183
 
1.0%
Other values (33) 6484
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 110660
 
10.6%
o 104102
 
10.0%
t 98840
 
9.5%
r 88410
 
8.5%
N 63273
 
6.1%
n 62000
 
6.0%
k 57751
 
5.5%
w 51568
 
5.0%
i 51429
 
4.9%
48546
 
4.7%
Other values (38) 304689
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 784300
75.3%
Uppercase Letter 197034
 
18.9%
Space Separator 48546
 
4.7%
Dash Punctuation 9425
 
0.9%
Connector Punctuation 1942
 
0.2%
Decimal Number 21
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 63273
32.1%
C 38199
19.4%
P 36723
18.6%
E 7844
 
4.0%
T 7459
 
3.8%
B 6240
 
3.2%
V 5440
 
2.8%
D 4580
 
2.3%
A 4579
 
2.3%
R 3926
 
2.0%
Other values (15) 18771
 
9.5%
Lowercase Letter
ValueCountFrequency (%)
e 110660
14.1%
o 104102
13.3%
t 98840
12.6%
r 88410
11.3%
n 62000
7.9%
k 57751
7.4%
w 51568
6.6%
i 51429
6.6%
a 47745
6.1%
g 35960
 
4.6%
Other values (9) 75835
9.7%
Space Separator
ValueCountFrequency (%)
48546
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9425
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1942
100.0%
Decimal Number
ValueCountFrequency (%)
7 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 981334
94.2%
Common 59934
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 110660
11.3%
o 104102
10.6%
t 98840
10.1%
r 88410
 
9.0%
N 63273
 
6.4%
n 62000
 
6.3%
k 57751
 
5.9%
w 51568
 
5.3%
i 51429
 
5.2%
a 47745
 
4.9%
Other values (34) 245556
25.0%
Common
ValueCountFrequency (%)
48546
81.0%
- 9425
 
15.7%
_ 1942
 
3.2%
7 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1041268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 110660
 
10.6%
o 104102
 
10.0%
t 98840
 
9.5%
r 88410
 
8.5%
N 63273
 
6.1%
n 62000
 
6.0%
k 57751
 
5.5%
w 51568
 
5.0%
i 51429
 
4.9%
48546
 
4.7%
Other values (38) 304689
29.3%

Latitude
Real number (ℝ)

Distinct63294
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.748262
Minimum0
Maximum64.852466
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:05.320798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.54306
Q134.028609
median38.379879
Q341.384056
95-th percentile45.442828
Maximum64.852466
Range64.852466
Interquartile range (IQR)7.3554474

Descriptive statistics

Standard deviation5.0564126
Coefficient of variation (CV)0.13395087
Kurtosis0.68937514
Mean37.748262
Median Absolute Deviation (MAD)3.858203
Skewness-0.37782299
Sum2501162.1
Variance25.567308
MonotonicityNot monotonic
2023-10-12T02:05:05.403478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.875902 28
 
< 0.1%
46.347582 19
 
< 0.1%
27.7720841 14
 
< 0.1%
37.6161599 12
 
< 0.1%
41.32 11
 
< 0.1%
35.9311 10
 
< 0.1%
37.419875 10
 
< 0.1%
46.351004 10
 
< 0.1%
47.658 9
 
< 0.1%
37.875985 8
 
< 0.1%
Other values (63284) 66128
99.8%
ValueCountFrequency (%)
0 4
< 0.1%
12.9218757 1
 
< 0.1%
13.18893574 1
 
< 0.1%
13.48592441 1
 
< 0.1%
17.995961 1
 
< 0.1%
17.99953 1
 
< 0.1%
18.009854 1
 
< 0.1%
18.086761 1
 
< 0.1%
18.094454 1
 
< 0.1%
18.099839 1
 
< 0.1%
ValueCountFrequency (%)
64.852466 1
< 0.1%
63.86892416 1
< 0.1%
63.74598652 1
< 0.1%
63.44725602 1
< 0.1%
63.38793423 1
< 0.1%
62.323961 1
< 0.1%
62.32242083 1
< 0.1%
62.3213 1
< 0.1%
62.16135021 1
< 0.1%
61.601874 1
< 0.1%

Longitude
Real number (ℝ)

Distinct63650
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-96.990696
Minimum-164.84885
Maximum77.344642
Zeros4
Zeros (%)< 0.1%
Negative66252
Negative (%)> 99.9%
Memory size517.8 KiB
2023-10-12T02:05:05.487835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-164.84885
5-th percentile-122.39308
Q1-118.09091
median-93.355143
Q3-79.117401
95-th percentile-71.533886
Maximum77.344642
Range242.1935
Interquartile range (IQR)38.973514

Descriptive statistics

Standard deviation19.579924
Coefficient of variation (CV)-0.20187425
Kurtosis-0.9243534
Mean-96.990696
Median Absolute Deviation (MAD)18.45754
Skewness-0.25737778
Sum-6426506.6
Variance383.37342
MonotonicityNot monotonic
2023-10-12T02:05:05.569933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.250055 28
 
< 0.1%
-119.277892 19
 
< 0.1%
-82.635926 14
 
< 0.1%
-122.3968687 12
 
< 0.1%
-72.07 11
 
< 0.1%
-122.206042 10
 
< 0.1%
-84.309978 10
 
< 0.1%
-122.712 9
 
< 0.1%
-122.250014 8
 
< 0.1%
-104.9012885 8
 
< 0.1%
Other values (63640) 66130
99.8%
ValueCountFrequency (%)
-164.848855 1
< 0.1%
-159.6721439 1
< 0.1%
-159.6714783 1
< 0.1%
-159.476393 1
< 0.1%
-159.46942 1
< 0.1%
-159.46862 1
< 0.1%
-159.4572297 1
< 0.1%
-159.456719 1
< 0.1%
-159.438976 1
< 0.1%
-159.43897 1
< 0.1%
ValueCountFrequency (%)
77.34464173 1
 
< 0.1%
76.43395548 1
 
< 0.1%
31.99100733 1
 
< 0.1%
0 4
< 0.1%
-8.77033 1
 
< 0.1%
-65.636934 1
 
< 0.1%
-65.67335 1
 
< 0.1%
-65.756678 1
 
< 0.1%
-65.7740519 1
 
< 0.1%
-65.79097 1
 
< 0.1%

Date Last Confirmed
Date

Distinct54
Distinct (%)0.1%
Missing110
Missing (%)0.2%
Memory size517.8 KiB
Minimum2019-12-12 00:00:00
Maximum2023-10-12 00:00:00
2023-10-12T02:05:05.654537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:05:05.747559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ID
Real number (ℝ)

UNIQUE 

Distinct66259
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186552.12
Minimum1517
Maximum312986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size517.8 KiB
2023-10-12T02:05:05.837327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1517
5-th percentile64353.9
Q1154467.5
median184582
Q3227914.5
95-th percentile307702.1
Maximum312986
Range311469
Interquartile range (IQR)73447

Descriptive statistics

Standard deviation64278.032
Coefficient of variation (CV)0.34455803
Kurtosis-0.16361595
Mean186552.12
Median Absolute Deviation (MAD)37416
Skewness-0.16930505
Sum1.2360757 × 1010
Variance4.1316654 × 109
MonotonicityStrictly increasing
2023-10-12T02:05:05.918665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1517 1
 
< 0.1%
213701 1
 
< 0.1%
213668 1
 
< 0.1%
213669 1
 
< 0.1%
213670 1
 
< 0.1%
213671 1
 
< 0.1%
213672 1
 
< 0.1%
213673 1
 
< 0.1%
213674 1
 
< 0.1%
213675 1
 
< 0.1%
Other values (66249) 66249
> 99.9%
ValueCountFrequency (%)
1517 1
< 0.1%
1519 1
< 0.1%
1523 1
< 0.1%
1525 1
< 0.1%
1531 1
< 0.1%
1552 1
< 0.1%
1556 1
< 0.1%
1572 1
< 0.1%
1573 1
< 0.1%
1583 1
< 0.1%
ValueCountFrequency (%)
312986 1
< 0.1%
312984 1
< 0.1%
312983 1
< 0.1%
312982 1
< 0.1%
312981 1
< 0.1%
312980 1
< 0.1%
312979 1
< 0.1%
312972 1
< 0.1%
312970 1
< 0.1%
312969 1
< 0.1%

Updated At
Date

Distinct4055
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
Minimum2021-03-11 23:22:17+00:00
Maximum2023-10-12 01:16:47+00:00
2023-10-12T02:05:06.008921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:05:06.099279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Owner Type Code
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing48259
Missing (%)72.8%
Memory size517.8 KiB
P
14950 
LG
 
1284
FG
 
1039
T
 
452
SG
 
267

Length

Max length2
Median length1
Mean length1.1438889
Min length1

Characters and Unicode

Total characters20590
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLG
2nd rowLG
3rd rowP
4th rowLG
5th rowLG

Common Values

ValueCountFrequency (%)
P 14950
 
22.6%
LG 1284
 
1.9%
FG 1039
 
1.6%
T 452
 
0.7%
SG 267
 
0.4%
J 8
 
< 0.1%
(Missing) 48259
72.8%

Length

2023-10-12T02:05:06.186827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T02:05:06.271003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
p 14950
83.1%
lg 1284
 
7.1%
fg 1039
 
5.8%
t 452
 
2.5%
sg 267
 
1.5%
j 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 14950
72.6%
G 2590
 
12.6%
L 1284
 
6.2%
F 1039
 
5.0%
T 452
 
2.2%
S 267
 
1.3%
J 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20590
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 14950
72.6%
G 2590
 
12.6%
L 1284
 
6.2%
F 1039
 
5.0%
T 452
 
2.2%
S 267
 
1.3%
J 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 20590
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 14950
72.6%
G 2590
 
12.6%
L 1284
 
6.2%
F 1039
 
5.0%
T 452
 
2.2%
S 267
 
1.3%
J 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 14950
72.6%
G 2590
 
12.6%
L 1284
 
6.2%
F 1039
 
5.0%
T 452
 
2.2%
S 267
 
1.3%
J 8
 
< 0.1%

Open Date
Date

Distinct3640
Distinct (%)5.5%
Missing148
Missing (%)0.2%
Memory size517.8 KiB
Minimum1995-08-30 00:00:00
Maximum2024-05-01 00:00:00
2023-10-12T02:05:06.353231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:05:06.441029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EV Connector Types
Categorical

IMBALANCE 

Distinct28
Distinct (%)< 0.1%
Missing37
Missing (%)0.1%
Memory size517.8 KiB
J1772
52138 
CHADEMO J1772COMBO
 
4502
TESLA
 
4108
J1772 TESLA
 
2257
J1772COMBO
 
1138
Other values (23)
 
2079

Length

Max length32
Median length5
Mean length6.5333424
Min length5

Characters and Unicode

Total characters432651
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCHADEMO J1772 J1772COMBO
2nd rowJ1772
3rd rowJ1772
4th rowCHADEMO J1772 J1772COMBO
5th rowCHADEMO J1772 J1772COMBO

Common Values

ValueCountFrequency (%)
J1772 52138
78.7%
CHADEMO J1772COMBO 4502
 
6.8%
TESLA 4108
 
6.2%
J1772 TESLA 2257
 
3.4%
J1772COMBO 1138
 
1.7%
CHADEMO J1772 J1772COMBO 851
 
1.3%
CHADEMO J1772 293
 
0.4%
NEMA515 215
 
0.3%
J1772 J1772COMBO 178
 
0.3%
NEMA1450 150
 
0.2%
Other values (18) 392
 
0.6%

Length

2023-10-12T02:05:06.638382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j1772 55932
74.2%
j1772combo 6693
 
8.9%
tesla 6389
 
8.5%
chademo 5707
 
7.6%
nema515 328
 
0.4%
nema520 187
 
0.2%
nema1450 172
 
0.2%

Most occurring characters

ValueCountFrequency (%)
7 125250
28.9%
1 63125
14.6%
2 62812
14.5%
J 62625
14.5%
O 19093
 
4.4%
M 13087
 
3.0%
A 12783
 
3.0%
E 12783
 
3.0%
C 12400
 
2.9%
9186
 
2.1%
Other values (10) 39507
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 252733
58.4%
Uppercase Letter 170732
39.5%
Space Separator 9186
 
2.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 62625
36.7%
O 19093
 
11.2%
M 13087
 
7.7%
A 12783
 
7.5%
E 12783
 
7.5%
C 12400
 
7.3%
B 6693
 
3.9%
T 6389
 
3.7%
S 6389
 
3.7%
L 6389
 
3.7%
Other values (3) 12101
 
7.1%
Decimal Number
ValueCountFrequency (%)
7 125250
49.6%
1 63125
25.0%
2 62812
24.9%
5 1015
 
0.4%
0 359
 
0.1%
4 172
 
0.1%
Space Separator
ValueCountFrequency (%)
9186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 261919
60.5%
Latin 170732
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 62625
36.7%
O 19093
 
11.2%
M 13087
 
7.7%
A 12783
 
7.5%
E 12783
 
7.5%
C 12400
 
7.3%
B 6693
 
3.9%
T 6389
 
3.7%
S 6389
 
3.7%
L 6389
 
3.7%
Other values (3) 12101
 
7.1%
Common
ValueCountFrequency (%)
7 125250
47.8%
1 63125
24.1%
2 62812
24.0%
9186
 
3.5%
5 1015
 
0.4%
0 359
 
0.1%
4 172
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 432651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 125250
28.9%
1 63125
14.6%
2 62812
14.5%
J 62625
14.5%
O 19093
 
4.4%
M 13087
 
3.0%
A 12783
 
3.0%
E 12783
 
3.0%
C 12400
 
2.9%
9186
 
2.1%
Other values (10) 39507
 
9.1%

Country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.8 KiB
US
66259 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters132518
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 66259
100.0%

Length

2023-10-12T02:05:06.708974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T02:05:06.777741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
us 66259
100.0%

Most occurring characters

ValueCountFrequency (%)
U 66259
50.0%
S 66259
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 132518
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 66259
50.0%
S 66259
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 66259
50.0%
S 66259
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 66259
50.0%
S 66259
50.0%

Access Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size517.8 KiB
public
62529 
private
 
3729

Length

Max length7
Median length6
Mean length6.05628
Min length6

Characters and Unicode

Total characters401277
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprivate
2nd rowprivate
3rd rowpublic
4th rowprivate
5th rowprivate

Common Values

ValueCountFrequency (%)
public 62529
94.4%
private 3729
 
5.6%
(Missing) 1
 
< 0.1%

Length

2023-10-12T02:05:06.835043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T02:05:06.906646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
public 62529
94.4%
private 3729
 
5.6%

Most occurring characters

ValueCountFrequency (%)
p 66258
16.5%
i 66258
16.5%
u 62529
15.6%
b 62529
15.6%
l 62529
15.6%
c 62529
15.6%
r 3729
 
0.9%
v 3729
 
0.9%
a 3729
 
0.9%
t 3729
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 401277
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 66258
16.5%
i 66258
16.5%
u 62529
15.6%
b 62529
15.6%
l 62529
15.6%
c 62529
15.6%
r 3729
 
0.9%
v 3729
 
0.9%
a 3729
 
0.9%
t 3729
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 401277
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 66258
16.5%
i 66258
16.5%
u 62529
15.6%
b 62529
15.6%
l 62529
15.6%
c 62529
15.6%
r 3729
 
0.9%
v 3729
 
0.9%
a 3729
 
0.9%
t 3729
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 66258
16.5%
i 66258
16.5%
u 62529
15.6%
b 62529
15.6%
l 62529
15.6%
c 62529
15.6%
r 3729
 
0.9%
v 3729
 
0.9%
a 3729
 
0.9%
t 3729
 
0.9%

Facility Type
Text

MISSING 

Distinct63
Distinct (%)0.4%
Missing49147
Missing (%)74.2%
Memory size517.8 KiB
2023-10-12T02:05:07.104540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length18
Mean length9.3599813
Min length3

Characters and Unicode

Total characters160168
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUTILITY
2nd rowUTILITY
3rd rowPARKING_GARAGE
4th rowUTILITY
5th rowUTILITY
ValueCountFrequency (%)
hotel 2918
17.1%
car_dealer 2792
16.3%
office_bldg 913
 
5.3%
fed_gov 864
 
5.0%
parking_lot 848
 
5.0%
muni_gov 730
 
4.3%
public 671
 
3.9%
shopping_center 611
 
3.6%
utility 513
 
3.0%
college_campus 507
 
3.0%
Other values (53) 5745
33.6%
2023-10-12T02:05:07.415200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 18481
11.5%
R 13821
 
8.6%
A 13631
 
8.5%
L 12331
 
7.7%
_ 11293
 
7.1%
T 10435
 
6.5%
O 10394
 
6.5%
I 9126
 
5.7%
G 8081
 
5.0%
C 7588
 
4.7%
Other values (13) 44987
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 148875
92.9%
Connector Punctuation 11293
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 18481
12.4%
R 13821
 
9.3%
A 13631
 
9.2%
L 12331
 
8.3%
T 10435
 
7.0%
O 10394
 
7.0%
I 9126
 
6.1%
G 8081
 
5.4%
C 7588
 
5.1%
N 7427
 
5.0%
Other values (12) 37560
25.2%
Connector Punctuation
ValueCountFrequency (%)
_ 11293
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 148875
92.9%
Common 11293
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 18481
12.4%
R 13821
 
9.3%
A 13631
 
9.2%
L 12331
 
8.3%
T 10435
 
7.0%
O 10394
 
7.0%
I 9126
 
6.1%
G 8081
 
5.4%
C 7588
 
5.1%
N 7427
 
5.0%
Other values (12) 37560
25.2%
Common
ValueCountFrequency (%)
_ 11293
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 18481
11.5%
R 13821
 
8.6%
A 13631
 
8.5%
L 12331
 
7.7%
_ 11293
 
7.1%
T 10435
 
6.5%
O 10394
 
6.5%
I 9126
 
5.7%
G 8081
 
5.0%
C 7588
 
4.7%
Other values (13) 44987
28.1%

EV Pricing
Text

MISSING 

Distinct753
Distinct (%)5.6%
Missing52704
Missing (%)79.5%
Memory size517.8 KiB
2023-10-12T02:05:07.710832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length150
Median length4
Mean length9.1770564
Min length3

Characters and Unicode

Total characters124395
Distinct characters72
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique523 ?
Unique (%)3.9%

Sample

1st rowFree
2nd rowFree; parking fee
3rd rowFree
4th rowFree; parking fee
5th rowFree
ValueCountFrequency (%)
free 11000
45.8%
fee 1866
 
7.8%
parking 1351
 
5.6%
per 1348
 
5.6%
variable 700
 
2.9%
kwh 669
 
2.8%
energy 356
 
1.5%
hour 266
 
1.1%
1 260
 
1.1%
for 258
 
1.1%
Other values (618) 5922
24.7%
2023-10-12T02:05:08.117241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 27479
22.1%
r 16180
13.0%
F 12635
 
10.2%
10442
 
8.4%
0 5668
 
4.6%
$ 3976
 
3.2%
a 3606
 
2.9%
i 3496
 
2.8%
. 3444
 
2.8%
n 3047
 
2.4%
Other values (62) 34422
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71099
57.2%
Uppercase Letter 20538
 
16.5%
Decimal Number 11510
 
9.3%
Space Separator 10444
 
8.4%
Other Punctuation 5842
 
4.7%
Currency Symbol 3976
 
3.2%
Dash Punctuation 786
 
0.6%
Math Symbol 154
 
0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%
Other values (3) 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27479
38.6%
r 16180
22.8%
a 3606
 
5.1%
i 3496
 
4.9%
n 3047
 
4.3%
k 2440
 
3.4%
g 1940
 
2.7%
p 1888
 
2.7%
h 1719
 
2.4%
o 1555
 
2.2%
Other values (14) 7749
 
10.9%
Uppercase Letter
ValueCountFrequency (%)
F 12635
61.5%
E 2267
 
11.0%
P 1296
 
6.3%
H 1222
 
5.9%
W 1055
 
5.1%
R 953
 
4.6%
V 713
 
3.5%
C 101
 
0.5%
D 98
 
0.5%
L 93
 
0.5%
Other values (10) 105
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 5668
49.2%
1 1455
 
12.6%
5 1143
 
9.9%
2 1118
 
9.7%
3 730
 
6.3%
9 614
 
5.3%
4 338
 
2.9%
7 199
 
1.7%
8 180
 
1.6%
6 65
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 3444
59.0%
/ 1583
27.1%
; 328
 
5.6%
, 319
 
5.5%
: 167
 
2.9%
# 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10442
> 99.9%
  2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 153
99.4%
= 1
 
0.6%
Control
ValueCountFrequency (%)
4
50.0%
4
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 3976
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 786
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Other Symbol
ValueCountFrequency (%)
℠ 3
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91637
73.7%
Common 32758
 
26.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27479
30.0%
r 16180
17.7%
F 12635
13.8%
a 3606
 
3.9%
i 3496
 
3.8%
n 3047
 
3.3%
k 2440
 
2.7%
E 2267
 
2.5%
g 1940
 
2.1%
p 1888
 
2.1%
Other values (34) 16659
18.2%
Common
ValueCountFrequency (%)
10442
31.9%
0 5668
17.3%
$ 3976
 
12.1%
. 3444
 
10.5%
/ 1583
 
4.8%
1 1455
 
4.4%
5 1143
 
3.5%
2 1118
 
3.4%
- 786
 
2.4%
3 730
 
2.2%
Other values (18) 2413
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124387
> 99.9%
Letterlike Symbols 3
 
< 0.1%
Punctuation 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27479
22.1%
r 16180
13.0%
F 12635
 
10.2%
10442
 
8.4%
0 5668
 
4.6%
$ 3976
 
3.2%
a 3606
 
2.9%
i 3496
 
2.8%
. 3444
 
2.8%
n 3047
 
2.4%
Other values (59) 34414
27.7%
Letterlike Symbols
ValueCountFrequency (%)
℠ 3
100.0%
Punctuation
ValueCountFrequency (%)
’ 3
100.0%
None
ValueCountFrequency (%)
  2
100.0%

Interactions

2023-10-12T02:04:57.931243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.597578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.017884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.598752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.038955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.489332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:58.009540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.681106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.084888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.670119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.114941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.564791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:58.087420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.744376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.165876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.772229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.195205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.643047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:58.159098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.809022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.251020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.837652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.265205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.713942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:58.235491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.876876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.331495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.902715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.340917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.790429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:58.309867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:55.945244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.525716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:56.969162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.414447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T02:04:57.858920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-12T02:05:08.215336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountLatitudeLongitudeIDStatus CodeGroups With Access CodeEV NetworkOwner Type CodeEV Connector TypesAccess Code
EV Level1 EVSE Num1.0000.667-0.220-0.1560.099-0.0050.0000.1140.0000.0590.0540.020
EV Level2 EVSE Num0.6671.0000.1070.0370.0720.0370.0070.0490.0900.0220.0730.071
EV DC Fast Count-0.2200.1071.000-0.095-0.011-0.1220.0520.0170.2100.0090.2150.000
Latitude-0.1560.037-0.0951.0000.2320.0080.0320.0210.1000.0690.0520.000
Longitude0.0990.072-0.0110.2321.0000.0270.0190.0400.1240.0740.0340.034
ID-0.0050.037-0.1220.0080.0271.0000.0690.2120.3420.2220.2400.299
Status Code0.0000.0070.0520.0320.0190.0691.0001.0000.1990.0570.0880.058
Groups With Access Code0.1140.0490.0170.0210.0400.2121.0001.0000.1960.3680.1401.000
EV Network0.0000.0900.2100.1000.1240.3420.1990.1961.0000.2130.2860.524
Owner Type Code0.0590.0220.0090.0690.0740.2220.0570.3680.2131.0000.2910.500
EV Connector Types0.0540.0730.2150.0520.0340.2400.0880.1400.2860.2911.0000.315
Access Code0.0200.0710.0000.0000.0340.2990.0581.0000.5240.5000.3151.000

Missing values

2023-10-12T02:04:58.470311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-12T02:04:58.817477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-12T02:04:59.281684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Fuel Type CodeStation NameStreet AddressCityStateZIPStatus CodeGroups With Access CodeAccess Days TimeCards AcceptedEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV NetworkLatitudeLongitudeDate Last ConfirmedIDUpdated AtOwner Type CodeOpen DateEV Connector TypesCountryAccess CodeFacility TypeEV Pricing
0ELECLADWP - Truesdale Center11797 Truesdale StSun ValleyCA91352EPrivateFleet use onlyNaNNaN57.02.0SHELL_RECHARGE34.248319-118.3879712023-09-1415172023-09-14 14:01:49 UTCLG1999-10-15CHADEMO J1772 J1772COMBOUSprivateUTILITYNaN
1ELECLADWP - West LA District Office1394 S Sepulveda BlvdLos AngelesCA90024EPrivateNaNNaNNaN4.0NaNNon-Networked34.052542-118.4485042023-01-1015192023-02-15 22:45:41 UTCLG2020-02-28J1772USprivateUTILITYFree
2ELECLos Angeles Convention Center1201 S Figueroa StLos AngelesCA90015EPublic5:30am-9pm; pay lotNaNNaN7.0NaNNon-Networked34.040539-118.2713872023-01-1015232023-02-14 15:54:11 UTCP1995-08-30J1772USpublicPARKING_GARAGEFree; parking fee
3ELECLADWP - John Ferraro Building111 N Hope StLos AngelesCA90012EPrivateFor fleet and employee use onlyNaNNaN338.012.0Non-Networked34.059133-118.2485892023-09-1415252023-09-14 14:01:49 UTCLG1999-10-15CHADEMO J1772 J1772COMBOUSprivateUTILITYNaN
4ELECLADWP - Haynes Power Plant6801 E 2nd StLong BeachCA90803EPrivateFleet use onlyNaNNaN19.01.0Non-Networked33.759802-118.0966652023-01-1015312023-02-15 22:45:41 UTCLG2018-05-01CHADEMO J1772 J1772COMBOUSprivateUTILITYNaN
5ELECLADWP - Harbor Generating Station161 N Island AveWilmingtonCA90744EPrivateFleet use onlyNaNNaN10.0NaNNon-Networked33.770508-118.2656282023-01-1015522023-02-15 22:45:41 UTCLG1999-10-15J1772USprivateUTILITYNaN
6ELECLADWP - Sylmar West13201 Sepulveda BlvdSylmarCA91342EPrivate - Government onlyFleet use onlyNaNNaN2.0NaNNon-Networked34.303090-118.4805052023-01-1015562023-02-15 22:45:41 UTCLG2016-01-01J1772USprivateUTILITYNaN
7ELECLADWP - EV Service Center1630 N Main StLos AngelesCA90012EPrivateFleet and employee use onlyNaNNaN46.01.0Non-Networked34.066801-118.2276052023-01-1015722023-02-15 22:45:41 UTCLG1999-10-15CHADEMO J1772USprivateUTILITYNaN
8ELECLADWP - Fairfax Center2311 S Fairfax AveLos AngelesCA90016EPrivateFleet use onlyNaNNaN13.0NaNNon-Networked34.036777-118.3688412023-01-1015732023-02-15 22:45:41 UTCLG2019-04-01J1772USprivateUTILITYNaN
9ELECCalifornia Air Resources Board9530 Telstar AveEl MonteCA91731EPublic24 hours dailyNaNNaN3.0NaNNon-Networked34.068720-118.0640002022-09-1415832023-02-14 15:54:11 UTCSG1996-10-15J1772USpublicSTATE_GOVFree
Fuel Type CodeStation NameStreet AddressCityStateZIPStatus CodeGroups With Access CodeAccess Days TimeCards AcceptedEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV NetworkLatitudeLongitudeDate Last ConfirmedIDUpdated AtOwner Type CodeOpen DateEV Connector TypesCountryAccess CodeFacility TypeEV Pricing
66249ELECOBE POWER MPA JX BLUE R1120 NW 20th StMiamiFL33127EPublic24 hours dailyNaNNaN2.0NaNChargePoint Network25.794302-80.2124802023-10-123129692023-10-12 00:54:20 UTCNaN2023-10-12J1772USpublicNaNNaN
66250ELECOBE POWER MPA JX BLUE L1120 NW 20th StMiamiFL33127EPublic24 hours dailyNaNNaN2.0NaNChargePoint Network25.794292-80.2125202023-10-123129702023-10-12 00:54:21 UTCNaN2023-10-12J1772USpublicNaNNaN
66251ELECOTEC OTEC OFFICE JD400 Patterson Bridge RdJohn DayOR97845EPublic24 hours dailyNaNNaNNaN1.0ChargePoint Network44.422108-118.9735102023-10-123129722023-10-12 00:54:21 UTCNaN2023-10-12CHADEMO J1772COMBOUSpublicNaNNaN
66252ELECFubonn Shopping Center2850 SE 82 nd AvePortlandOR97266PPLANNED - not yet accessible (Public)NaNNaNNaN6.0NaNOpConnect45.501790-122.5769502023-10-123129792023-10-12 01:03:33 UTCNaN2023-10-12J1772USpublicNaNNaN
66253ELECJim Stykemain Chevrolet Buick GMC810 W Chicago RdSturgisMI49091EPublicNaNNaNNaNNaN1.0EV Connect41.797237-85.4294172023-10-123129802023-10-12 01:05:06 UTCNaN2023-10-12J1772COMBOUSpublicNaNNaN
66254ELECSandman Bros56 E Broadway StShelbyvilleIN46176EPublicNaNNaNNaNNaN1.0EV Connect39.523387-85.7750742023-10-123129812023-10-12 01:05:23 UTCNaN2023-10-12J1772COMBOUSpublicNaNNaN
66255ELECBatesville Shopping Village151 Batesville Shopping Vlg, Batesville, IN 47006, United StatesBatesvilleIN47006EPublicNaNNaNNaNNaN3.0EV Connect39.290234-85.1953292023-10-123129822023-10-12 01:05:23 UTCNaN2023-10-12CHADEMO J1772COMBOUSpublicNaNNaN
66256ELECPatriot Chevrolet - Bartlesville3800 SE Adams RdBartlesvilleOK74006EPublicNaNNaNNaNNaN1.0EV Connect36.745702-95.9337882023-10-123129832023-10-12 01:05:23 UTCNaN2023-10-12J1772COMBOUSpublicNaNNaN
66257ELECLeadCar Chevrolet Yorkville5043 Commercial DriveYorkvilleNY13495EPublicNaNNaNNaNNaN2.0EV Connect43.108335-75.2958842023-10-123129842023-10-12 01:05:23 UTCNaN2023-10-12J1772COMBOUSpublicNaNNaN
66258ELECLeisure Centre - City of Chilliwack -49291 Corbould StChilliwackBCV2P 4EPublic24 hours dailyNaNNaN1.0NaNFLO49.171107-121.9646682023-10-123129862023-10-12 01:16:47 UTCNaN2023-10-12J1772USpublicNaNNaN